Weather Services

for Land Transport

in Hong KongAD HOC EXPERT TEAM MEETING ON METEOROLOGICAL SERVICES ON LAND TRANSPORTATION

GENEVA, SWITZERLAND, 18-19 MARCH 2019

YEUNG, Hon Yin

Land Transport in Hong KongMAIN BRIDGES

2

Main Bridges in Hong Kong3

Shenzhen Bay Bridge (Hong Kong-Shenzhen Western Corridor)

since 2007

Tsing Ma Bridge(Tsing Ma Control Area)

since 1997

Stonecutters Bridgesince 2009

The Newest Bridge in Hong Kong4

Hong Kong-Zhuhai-Macao Bridge(HZMB)

since October 2018

HK Section: Hong Kong Link Road

Zhuhai

Hong Kong

Macao

On-bridge Met Sensors5

Bridge Length(km) Wind Rain Temperature Visibility

Tsing Ma 1.38 ü

Shenzhen Bay5.5

(HK section: 3.5)

ü ü ü ü

Stonecutters 1.6 ü

HZMB29.6

(HK section: 12)

ü ü

Service Example – Tsing Ma Bridge6

Completely closed when 10-min mean

wind speed > 190 km/h

2,160 m long (main span 1,377 m)206 m high (height of towers)

Service Example – Shenzhen Bay Bridge

7

Service Example – Hong Kong Link Road

8

HKO’s Services in Support of Bridge Traffic Management

u Monitor weather conditions at/near the bridges

u Including wind speed, visibility and sea level

u Provide forecasts on wind trend to bridge management authorities (on request)

u Rising? subsiding? steady?

u Liaise with emergency management departments according to set meteorological conditions / criteria

u (HZMB) Provide a GIS platform for information sharing and common situation awareness

9

Example - Rainstorm on 3 Mar 19 10

Example - Rainstorm on 3 Mar 19 11

Warning messages in CAP format

Other Weather Challenges - Fog12

Dense sea fog affecting the HK Int’l Airport on 25 Dec 2009 – close to the HZMB areas

Other Weather Challenges - Fog13

Visibility rather low over the western part of Hong Kong and the Pearl River Estury

Land Transport in Hong KongCOMMUNICATIONS WITH KEY STAKEHOLDERS

14

Emergency Communications during Inclement Weather

15

HKO

OFCA

SB

TD

Public Transport Operators

DSD

CEDD

HAD

ISD

FSD

EDB

Emergency Communications during Tropical Cyclones

16

HKO

OFCA

SB

Being informed about 2 hours before issue/ downgrade of No.8

TC Signal Assessment Update in categories of probability (When No.3 or above in force)

Early alert of No.9

DSD

CEDD

HAD

ISD

FSD

EDB

Critical Timings during Tropical Cyclones

17

Tropical Cyclone Signal Assessment Update

about 2 h

Precursor to Pre-No. 8 Pre-No. 8

about 0.5 h

Transportation Shutdown

Standby Strong Wind

Gale or Storm

TC Signal Assessment

u Assessment on the chance of TC signal change expressed in terms of probability categories:

u Low, medium-low, medium, medium-high, high

u For the next 6 hours

u Updated at scheduled times and when necessary

u For internal use with Transport Department

18

Special Webpage for Transport Dept19

HKO’s Weather App - MyObservatory20

Case Study - SupT Mangkhut

u Post-disaster recovery still a big challenge

u Need for impact forecast

u how many trees/structures will fall?

u which critical road/rail sections are likely to be blocked?

21

Research & DevelopmentIMPACT OF HEAVY RAIN ON TRAFFIC SPEED

22

Joint Pilot Project to Study the Impact of Heavy Rain on Traffic Speed

u Joint venture on big data between 3 government depts:

u Hong Kong Observatory

u Transport Department

u Office of Government Chief Information Officer

u To forecast traffic speed at individual road segments in the next 30 min to one hour

23

Roads with Speed Sensors24

Traffic Speed vs Rainfall25

HKO’s SWIRLS Nowcasting System26

HKO designated as an RSMC for Nowcasting for the Asian region at the 70th EC of WMOhttps://rsmc.hko.gov.hk/nowcast/

Radar-based Rainfall Nowcast

u Detailed rainfall distribution up to 6 hours ahead

u Radar echo extrapolation based optical flow tracking with rain-rate calibrated by raingauge data

u Deep-learning version under trial

27

Location-based Nowcast Service

u Available on mobile app

u Rainfall nowcast for the next 2 hours at user’s location

u data from SWIRLS rainfall nowcast

u Personalized automatic alerting service based on user location and expected rainfall

28

Weather Impact on Traffic –Model based on Machine Learning

29

Traffic Speed data

Rainfall data

Time dependent

factors(Day of week, holiday, etc.)

Training Set(4/6)

Validation Set (1/6)

Test Set (1/6)

ArtificialNeural

Network Model

Trainmodel

Validatemodel

Test model

Predicted Traffic Speed of next hour

Accuracy(Actual vs Predicted)

(1) Unsupervised Learning

u Curse of Dimensionality

u 610 (roads) x 288 (5-min traffic speed) x 7 (Day of week) x 2 (public holiday or not) x … > 2,459,520

u Clustering based on traffic speed pattern (correlation)

u t-distributed stochastic neighborhood embedding (t-SNE)

30

Sun

MonThu

Sat

Tue

Fri

Wed

(2) Supervised Learning

u 610 Road Segments grouped to 79 clusters of adjacent road segments

u A 2-layer Artificial Neural Network (ANN) developed for road segments

in each cluster

u One to predict speed after 30 min, another to predict speed after 1 hr

31

X: Input (n x 1 vector)

Current Speed

(of s road segment in a cluster)

Past Hour Rainfall

(of s road segment in a cluster)

Next 1 hour or 30 min Rainfall

(of s road segment in a cluster)

Hours, Minutes, Day of week,

Holiday

Y: Output (s x 1 vector)

Predicted Speed

(of s road segment in a cluster)

Size of ANNsn: 50+ q: 20 - 1060r: 10 - 530 s: ≤ 53

X H1 H2 YFeed-forward neural network with two hidden layers of neurons

Example - Before Raining32

Example - After Raining33

Zoom-in (Case of 2016.04.13)34

Rainfall in 5 min (m

m)

Predicted speedActual speed

Predicted speed with r/f nowcastAverage speed

Actual speed

Rainfall

Average speed

Preliminary Resultu Not yet operationally in use

u Generally speaking, more than 90% of all 610 road segments covered in this study has a traffic speed prediction accuracy larger than 80% in 2016

35

0%10%20%30%40%50%60%70%80%90%

100%

All

timeslots

No rainfall

(<=0.5

mm)

All rainfall

(>0.5mm)

Light

rainfall

(>0.5 –10mm)

Medium

rainfall

(>10 –30mm)

Heavy

rainfall

(>30mm)Ro

ad

Se

gme

nts

wit

h 8

0%

Acc

ura

cy

Hourly Rainfall (mm)

Prediction Accuracy of Neural Network Models

Baseline

Neural Network (Predict 1hrlater)Neural Network (Predict 30minlater)

Lower accuracy due to scarcity of heavy rainfall data

Number of observations with heavy rainfall in 2016: 74859

(0.12%)

à More heavy rainfall training

data can improve the accuracy

More accurate than baseline when raining

Possible Ways Forward

u Install more speed sensors to cover more roads

u Employ crowd-sourcing technology to derive a real-time traffic map

u Collect other impact data such as flooding, traffic incidents, etc.

u Further develop the ANN model to extend the coverage of the road network in the territory

36

Crowd-sourcing Traffic Speed Data Based on Mobile App

u Mobile App to provide data

u Needs best accuracy for location à GPS

u Road information à Map

u Users report road accidents?

u Servers to collect data

u Real-time traffic à Many requests

u Track logs à Volume

u Data filtering à Processing Power

37

Insight from “MyObservatory” App

u Rainfall Nowcast

u Rain coming within 2 hours è Push notification

u Mechanism:

u 35 x 31 grids (Coverage of the rainfall forecast )

u User’s position(lat., lon.) polling to our servers è fall into one of the grids

u Rain will happen in the grid è Push

u How to become crowdsourcing?

u Record a time series of the user’s positions è Track logs

38

Effectiveness - Access Statistics39

“MyObservatory” App –Over 7.6 million downloads since launch in 2010

Research & DevelopmentCROWDSOURCING OF TRAFFIC IMPACT DATA

40

Traffic Analytics from Online News41

� Input: online traffic news in text

“�� �����������: ��.”

� Output: traffic-segment on GIS

Deep-learning Neural Networks(Natural language processing)

Traffic News Analytics - Example 42

Input: online traffic news Output: highlighted road-segments on GIS map (red: rain/flood related; green: other traffic incidents)

Traffic Analytics - Details43

Click on the segment to see details

Flexibility for showing flood or heavy rain related news

Data Pre-processingu Training data set

u 1-year past data (about 20,000)

u Data cleaning

u Remove garbled text

u Identify hidden issue within data, e.g. unbalance data distribution

u Prepare dataset

u For classifier, manually classify data by types

u For named-entity recognition (NER), add tagging in IOB format

44

� � � � � � � � ,B-QR I-QR I-QR I-QR O B-DD I-DD O O O � � � � � � � �

B-DE I-DE I-DE O O O O O O O, � � : � � �

O O O O B-DS I-DS O

u Traffic pattern recognition using deep learning

Data Mining with Traffic Cam?45

weather recognition as well?

For Discussion –Challenges & OpportunitiesTHE NEEDS OF FUTURE LAND TRANSPORTATION MEANS

¾ AUTONOMOUS VEHICLES

46

Autonomous Vehiclesu 6 Levels of Driving Automation:

47

Source – US Dept of Transportation (https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety)

Autonomous Vehiclesu In layman terms:

u Level 0 – fully manual

u Level 1 – “hands on”

u Level 2 – “hands off”

u Level 3 – “eyes off”

u Level 4 – “mind off”

u Level 5 – “steering wheel optional”

u Existing “autopilot” functions

u Level 2 autonomous

u Somewhat weather sensitive from personal experience

48

Autonomous Vehiclesu What weather service will be needed for different

levels of automation?

u US Department of Transportation:

u “Access to data is a critical enabler for the safe, efficient, and accessible integration of AVs into the transportation system. Lack of access to data could impede AV integration and delay their safe introduction”

u Data exchange

u what weather/vehicle data are required?

u Frequency, latency and volume requirements?

u Data API?

u How to realize the data transfer?

49

Data for Autonomous Vehiclesu US Dept of Transportation:

50

Source - https://www.transportation.gov/sites/dot.gov/files/docs/policy-initiatives/automated-vehicles/311186/draftdaviframework.pdf

Voice from One of the Stakeholders51

Source - https://www.tesla.com/en_AE/blog/master-plan-part-deux

The End

52